Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction.

Sun Q, Feng H, Yan X, Zeng Z - Sensors (Basel) (2015)

Bottom Line:
The recognition accuracy and speed of current systems cannot meet the requirements of Φ-OTDR online vibration monitoring systems.Feature vectors are obtained from morphologic features of time-space domain signals.A scatter matrix is calculated for the feature selection.

ABSTRACTThis paper proposes a novel feature extraction method for intrusion event recognition within a phase-sensitive optical time-domain reflectometer (Φ-OTDR) sensing system. Feature extraction of time domain signals in these systems is time-consuming and may lead to inaccuracies due to noise disturbances. The recognition accuracy and speed of current systems cannot meet the requirements of Φ-OTDR online vibration monitoring systems. In the method proposed in this paper, the time-space domain signal is used for feature extraction instead of the time domain signal. Feature vectors are obtained from morphologic features of time-space domain signals. A scatter matrix is calculated for the feature selection. Experiments show that the feature extraction method proposed in this paper can greatly improve recognition accuracies, with a lower computation time than traditional methods, i.e., a recognition accuracy of 97.8% can be achieved with a recognition time of below 1 s, making it is very suitable for Φ-OTDR system online vibration monitoring.

Mentions:
The signal calculated by Equation (4) and the experimentally measured signals of three pipeline safety events are shown in Figure 4, Figure 5 and Figure 6. Due to the long distance of the Φ-OTDR system monitoring, the vibration range is relatively small. An intrusion event is usually a fleck in the time-space domain image. The intrusion event occurs continuously in practice, so there are not only one event region in general in time-space signal image. Usually, walking frequency is three steps within 2 s, the interval between the wheels of the vehicle pressing the deceleration strip is about 0.25 s, and the interval due to digging is much larger than 2 s in the experiment. There are three event regions A1, A2, A3 in the experimental walking event, A2, A3 are repetition of A1. There are only one event region B1 in the experimental digging event, and three event regions C1, C2, C3 in the vehicle passing event. Therefore events are calculated only once for simulation signal, marked as region A, region B and region C in Figure 4, Figure 5 and Figure 6. In Figure 4, Figure 5 and Figure 6, the graphs in (a) are the simulation signals calculated by Equation (4) and the graphs in (b) are the experimentally measured signals. In order to illustrate the problem more clearly, the simulation signals are appropriately amplified. In the simulation process, for calculation convenience, many environmental factors were ignored. Soil is idealized as elastic half-space [10], and intrusion events are idealized as single frequency signals. Although there are some differences between the simulation and experimental signal images, they are basically the same.

Mentions:
The signal calculated by Equation (4) and the experimentally measured signals of three pipeline safety events are shown in Figure 4, Figure 5 and Figure 6. Due to the long distance of the Φ-OTDR system monitoring, the vibration range is relatively small. An intrusion event is usually a fleck in the time-space domain image. The intrusion event occurs continuously in practice, so there are not only one event region in general in time-space signal image. Usually, walking frequency is three steps within 2 s, the interval between the wheels of the vehicle pressing the deceleration strip is about 0.25 s, and the interval due to digging is much larger than 2 s in the experiment. There are three event regions A1, A2, A3 in the experimental walking event, A2, A3 are repetition of A1. There are only one event region B1 in the experimental digging event, and three event regions C1, C2, C3 in the vehicle passing event. Therefore events are calculated only once for simulation signal, marked as region A, region B and region C in Figure 4, Figure 5 and Figure 6. In Figure 4, Figure 5 and Figure 6, the graphs in (a) are the simulation signals calculated by Equation (4) and the graphs in (b) are the experimentally measured signals. In order to illustrate the problem more clearly, the simulation signals are appropriately amplified. In the simulation process, for calculation convenience, many environmental factors were ignored. Soil is idealized as elastic half-space [10], and intrusion events are idealized as single frequency signals. Although there are some differences between the simulation and experimental signal images, they are basically the same.

Bottom Line:
The recognition accuracy and speed of current systems cannot meet the requirements of Φ-OTDR online vibration monitoring systems.Feature vectors are obtained from morphologic features of time-space domain signals.A scatter matrix is calculated for the feature selection.

ABSTRACTThis paper proposes a novel feature extraction method for intrusion event recognition within a phase-sensitive optical time-domain reflectometer (Φ-OTDR) sensing system. Feature extraction of time domain signals in these systems is time-consuming and may lead to inaccuracies due to noise disturbances. The recognition accuracy and speed of current systems cannot meet the requirements of Φ-OTDR online vibration monitoring systems. In the method proposed in this paper, the time-space domain signal is used for feature extraction instead of the time domain signal. Feature vectors are obtained from morphologic features of time-space domain signals. A scatter matrix is calculated for the feature selection. Experiments show that the feature extraction method proposed in this paper can greatly improve recognition accuracies, with a lower computation time than traditional methods, i.e., a recognition accuracy of 97.8% can be achieved with a recognition time of below 1 s, making it is very suitable for Φ-OTDR system online vibration monitoring.